The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements

Truly real-life data presents a strong, but exciting challenge for sentiment and emotion research. The high variety of possible `in-the-wild' properties makes large datasets such as these indispensable with respect to building robust machine learning models. A sufficient quantity of data covering a deep variety in the challenges of each modality to force the exploratory analysis of the interplay of all modalities has not yet been made available in this context. In this contribution, we present MuSe-CaR, a first of its kind multimodal dataset. The data is publicly available as it recently served as the testing bed for the 1st Multimodal Sentiment Analysis Challenge, and focused on the tasks of emotion, emotion-target engagement, and trustworthiness recognition by means of comprehensively integrating the audio-visual and language modalities. Furthermore, we give a thorough overview of the dataset in terms of collection and annotation, including annotation tiers not used in this year's MuSe 2020. In addition, for one of the sub-challenges - predicting the level of trustworthiness - no participant outperformed the baseline model, and so we propose a simple, but highly efficient Multi-Head-Attention network that exceeds using multimodal fusion the baseline by around 0.2 CCC (almost 50 % improvement).Comment: accepted versio

Towards a Model of Open and Reliable Cognitive Multiagent Systems: Dealing with Trust and Emotions

 Open multiagent systems are those in which the agents can enter or leave the system freely. In these systems any entity with unknown intention can occupy the environment. For this scenario trust and reputation mechanisms should be used to choose partners in order to request services or delegate tasks. Trust and reputation models have been proposed in the Multiagent Systems area as a way to assist agents to select good partners in order to improve interactions between them. Most of the trust and reputation models proposed in the literature take into account their functional aspects, but not how they affect the reasoning cycle of the agent. That is, under the perspective of the agent, a trust model is usually just a “black box” and the agents usually does not take into account their emotional state to make decisions as well as humans often do. As well as trust, agent’s emotions also have been studied with the aim of making the actions and reactions of the agents more like those of humans being in order to imitate their reasoning and decision making mechanisms. In this paper we analyse some proposed models found in the literature and propose a BDI and multi-context based agent model which includes emotional reasoning to lead trust and reputation in open multiagent systems

Affective Adaptation of Social Norms in Workplace Design

Open-plan offices are common in today's organisations. These types of workplaces require people to share a common space, where violation of (implicitly or explicitly stated) social norms can cause instances of incivility. If nothing is done to avoid these situations, bad feeling can lead to diminished productivity and cooperation, and, in the long-term, to more serious problems, such as conflict and aggression. A critical review of literature shows the effects of workplace incivility and the need for an internal reparation mechanism. Inspired by convergence of pervasive, adaptive and affective computing, we have designed and developed a self-regulatory platform for successful collective action, based on participatory adaptation and fair information practises, which we called MACS. MACS addresses the problem of incivility and aims at improving the Quality of Experience in shared workplaces. This thesis presents all studies that led to the development of MACS. Through the analysis of an online questionnaire we gathered information about incivility in shared workplaces, how people deal with those situations, and awareness about uncivil self-behaviours. We concluded the main issue while sharing a workplace is noise, and most people will try to change their own behaviour, rather than confronting the person being uncivil. MACS's avatar-based interface was developed with the purpose of heightening self-awareness and cueing the appropriate social norms, while providing a good User Experience (UX). Avatars created to people's image, rather than photos, were used, to keep MACS's tone light and relatively unintrusive, while still creating self-awareness. MACS's final version went through UX testing, where 6 people were filmed while performing tasks in MACS. The intended work-flow and user interfaces to support the smooth passage of the work-flow have been validated by the UX user testing. There is some preliminary evidence suggesting apology will elicit empathic responses in MACS. Finally, this thesis proposes guidelines for workplace design, which are founded on participatory creation and change of social norms, and ways to make sure they are enforced. In this sense, MACS can also be seen as a prototypical example of a socio-technical system being used as platform for successful collective action.Open Acces